Optimal Energy Storage Policies for Taming the Stochastic Grid

Abstract

We discuss the impact of storage in the electrical grid, both at the energy consumer and at the energy producer scale. In the first part of the talk, we consider an electricity consumer receiving Demand-Response signals from the network operator. The consumer needs to satisfy a non-elastic load (e.g., a data center). To this end, a perfect battery is interposed between the load and the grid. The battery plays the role of a buffer that renders service reductions transparent to the load. The operator guarantees a minimal quality of service to the consumer via a Service Curve Contract. We provide explicit necessary and sufficient conditions, which ensure the existence of a feasible battery charging schedule. Furthermore, we show that whenever a feasible schedule exists, it can be computed online. As an exercise, based on a real measured trace of Akamai traffic, we compute sufficient battery capacity for a typical server. In the second part of the talk, we consider optimal energy storage policies for mitigating wind prediction errors. We revisit a model of real storage proposed by Bejan et al. We study the impact on performance of energy conversion efficiency and of wind prediction quality. Specifically, we provide theoretical bounds on the trade-off between energy loss and fast ramping generation, which we show to be tight for large capacity of the available storage. Moreover, we develop strategies that outperform the proposed fixed level policies when evaluated on real data from the UK grid.